E2B

What is E2B?

E2B is a cloud runtime that gives an AI agent somewhere safe to run code. When a language model writes a Python script, something has to execute it, and executing model-written code on your own servers is a security problem you do not want. E2B’s answer is a sandbox built on Firecracker microVMs — the same isolation technology AWS Lambda uses — that boots in under 200ms and hands the agent a full Linux environment: a terminal, a filesystem, internet access, and the ability to install whatever packages it needs. The isolation is at the virtualization layer rather than a shared kernel, which is the distinction that matters when the code arrived from a model rather than a reviewer.

The product is model-agnostic infrastructure, not an assistant. You drive it from a Python or Node.js SDK, and it plugs in behind Anthropic, OpenAI, Mistral, Llama, LangChain, or LlamaIndex without caring which one you picked. Sandboxes run up to 1 hour on the free tier and 24 hours on Pro, can be paused and resumed from a filesystem snapshot, and can be pre-baked as custom templates so agents start with your dependencies already installed. Compute bills per second — $0.000014 per vCPU-second, roughly $0.0504 per vCPU-hour — so idle agents cost nothing. The SDK and the infrastructure are both Apache-2.0, and E2B publishes Terraform scripts for teams that want to run the whole stack in their own cloud account. That self-host path is genuine, but it is an infrastructure project: you end up operating a Nomad and Consul scheduling cluster, not flipping a billing toggle.

Who is it for?

E2B is for engineering teams building products where a model writes code that then has to actually run — code interpreters, data analysts, autonomous coding agents, computer-use agents. The free Hobby tier and its one-time $100 credit comfortably cover prototyping, but the 1-hour session cap means anything long-running jumps straight to the $150/month Pro plan. That base fee is the real decision point: per-second compute is cheap, so E2B makes sense when sandbox usage is steady rather than occasional, and looks expensive when it is spiky.

• Teams shipping code-interpreter or AI data-analyst features who need to execute generated pandas against customer files without that code touching their own infrastructure.
• Builders of autonomous coding agents that need a real shell to install packages, run tests, and iterate on failures — where the agent’s reasoning comes from a model like Claude and E2B supplies the box it works in.
• AI infrastructure engineers evaluating whether to buy sandboxing or build it, who want an Apache-2.0 exit ramp rather than a permanent dependency.
• Enterprises with data-residency constraints that need BYOC, on-premises, or fully self-hosted deployment and are willing to negotiate custom pricing for it.
• RL and eval teams spinning up large numbers of parallel isolated environments, where per-second billing beats keeping a fleet warm.